Automatic Parking Enforcer

1. Automatic Parking Enforcer ENSC 440 Presentation and Demo December 13, 2010 Presented by: R.Maroufi, R.Johal, A.Moshgabadi, Y.Kuo, S.Rohani

2. Rodin Maroufi (CEO) • - Image Processing Unit • - OCR • Rosy Johal (COO) • - GUI • - Documentation • Amin Moshgabadi (CTO) • - Image Processing Unit • - Infrared Camera • - OCR

3. Yi Chen Kuo (CFO) - GUI - Integration - Documentation • Shadi Rohani (CMO) - Image Processing Unit - OCR

4. Motivation • Background • System Overview • Business Case • Time Line • Learning Outcomes • Future Work • Conclusion • Acknowledgments • Question and Answer Period

5. Motivation Patrolling Large Parking Lots requires: - significant number of employees. - significant amount of money. - issuing individual paper permits. - students remembering to bring their permit. Image Credit: blog.telenav.com Image Credit:

6. Easy to Operate • Portable • Low Power • Low Cost • Recognize all North American License Plates • Operate in most weather conditions • Differentiate between different parking lots • Recognize more than one vehicle per permit

7. AutoVu - System software is incorporated in the camera. - Retail price is very expensive, about $20,000. - Only checks if the vehicle has been parked for longer than a certain time. - GUI does not have the manual search option. Image Credit: www.security-technologynews.com Image Credit: MatthiasKabel (Wikimedia Commons)

8. APE (Automatic Parking Enforcer) consists of five components: - Infrared Camera - Imaging Processing Unit - OCR (Optical Character Recognization) unit - Database - GUI

10. In most weather • Needs to operate in most weather conditions • Needs to work in low lighting. • Needs to be light and durable • Needs to be able to mount on top of car • Needs to be waterproof

11. Horizontal Resolution Color 1/3" Sony, 600TV Lines • IR LED 42PCS  • Lens 4-9mm Manual Zoom Lens • Operation Temperature -10~ +50degree RH95% Max • Able make Automatic Gain Control Off & Adjust shutter speed

12. Responsible for separating the image of the license plate from the image of the vehicle. • Coded using Visual C++ because: - ease of programming functionality - MFC support • Can be broken into two main components: - License Plate Recognization - Licesnse Plate Segmentation Image Credit: ALGORITHMIC AND MATHEMATICAL PRINCIPLES OF AUTOMATIC NUMBER PLATE RECOGNITION SYSTEMS

13. Responsible for recognize the License Plate from the image of a vehicle. • The input to the License Plate Recognizer is the clear image obtained from the infrared camera. • OPenCV library is used to process the grayscale image. Image Credit: ALGORITHMIC AND MATHEMATICAL PRINCIPLES OF AUTOMATIC NUMBER PLATE RECOGNITION SYSTEMS

15. Image of Morphological Edge Detection

16. Horizontal projection of the image is drawn to determine the boundaries of the image • The peak of the graph is calculated, the skirts are marked and width is attained • If width is less than 20% of the image width zero the interval and look for next peak

17. The approximate width is analyzed and the data is projected into the y axis. • Same concept as before is applied and the approximate height of the license plate is attained.

18. The purpose of License Plate Segmentation is to segment the License Plate once it has been recognized from an image of the whole vehicle. • Uses a skew correction algorithm to align the image properly. Image Credit: ALGORITHMIC AND MATHEMATICAL PRINCIPLES OF AUTOMATIC NUMBER PLATE RECOGNITION SYSTEMS

19. Once the approximate height and width are attained that segment is cropped out from the original image. • For the image to be able to be read by the OCR engine the image has to be processed again and the noise removed . • The x and y projection of the image are drawn and noises removed using the information on the graphs. Image Before and after noise filtering for OCR

21. The purpose of the OCR is to read the License plate characters from the segmented image. • Tesseract will be used as the OCR engine, because it was chosen as one of the top 3 engines a the UNLV Accuracy test. Image Credit: ALGORITHMIC AND MATHEMATICAL PRINCIPLES OF AUTOMATIC NUMBER PLATE RECOGNITION SYSTEMS

25. Our product is meant for large parking lots such as: - University Campuses - Large Buildings • It can also be used for security services, such as surveillance. • Similar, products are very expensive. Image Credit: greendairy.com

27. $ 400 was obtained from the ESSEF (Engineering Science Student Endowment Fund) • $ 50 was obtained from department funding. • $ 40 of our own funds was used. Image Credit: guarantyautos.com

29. Learned professionalism and team dynamics • Improved debugging and troubleshooting skills • Improved time management skills • Learned not to trust the supplier and always prepare for the worst case scenario • Learned the importance of integrating as soon as possible. Image Credit: tls.vu.edu.au

30. Improve filtering techniques or noise removal. • Use machine learning for Tesseract’s OCR • Use Skew correction • Get better quality camera to take videos • Add sensor to sense lighting condition and adjust threshold accordingly

31. Achieved a low power and low cost system. • APE is also easy to operate. • Can work in many weather conditions. • Can accommodate more than one vehicle per permit.

32. ESSEF (Funding) • David Agosti (Information) - Manager of Parking Services (SFU) • Faculty of SFU Engineering Science • Andrew Rawicz • Mike Sjoerdsma • Ali Ostadfar • Carlyn Loncaric

33. Image Credit: searchenginepeople.com